We first examine the models with automatic metrics, including perplexity (PPL), BLEU [1]}, \(\lbrace \text{Average}, \text{Extrema}, \text{Greedy}\rbrace \)  [2]}, and F1 [3]}. The last metric is calculated with the code shared at https://github.com/facebookresearch/ParlAI/blob/master/parlai/core/metrics.py, while the others are computed using a public NLG evaluation project available at https://github.com/Maluuba/nlg-eval. We choose Transformer Memory Network (TMN) [3]}
and Disentangled Response Decoder (DRD) [5]} as baselines, where DRD exploits pre-training techniques to handle the low-resource challenge in knowledge-grounded dialogue generation, and holds the state-of-the-art performance on Wizard when all parameters are fine-tuned with the full training data.
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